Peak heatmap
Bing Shui
2021-09-21
Prepare libraies
library(data.table)
library(tidyverse)
library(rtracklayer)
library(DESeq2)
library(Biostrings)
library(genomation)
library(RColorBrewer)
library(CLIPanalyze)Calculate scaleFactor for bamCoverage
bamCoverage from Deeptools was used to generated scaled bw files for plotting peak heatmaps
Scalefactors are calculated as the reciprical of DESeq2 estimated sizeFactors using counnts in genes outside of peaks.
dir.create("PDF_figure/Peak_heatmap_merged", showWarnings = FALSE)
load('../Merged_Analysis/merged_peak_analysis.rda')
sizefactor <- sizeFactors(dds.peaks.hf.hfk)
scalefactor <- (1/sizefactor)/max((1/sizefactor))
scalefactor## HF1 HF2 HF3 HFK1 HFK2 HFK3
## 0.73249964 1.00000000 0.44469856 0.37630368 0.07118698 0.24779184bw file generation was done on cluster.
Load miR-peaks dataset
mirs.peaks <- readRDS("Datafiles/miRNA-merged-peaks-list-12232019.rds")Analysis
Prepare bw file for visualization
# combine bigwig files of the same genotype
$ cd ../Rescaled analysis/HF
$ bigWigMerge *.bw HF_merged.bedGraph
$ cd ../Rescaled analysis/HFK
$ bigWigMerge *.bw HFK_merged.bedGraphconvert chromosome name from Ensembl to UCSC format.
chromosomename <- read.table("../Rescaled analysis/Ensembl.mm10.chrom.sizes.txt", sep = "\t")[,1][1:22]
hf_bed <- read.table("../Rescaled analysis/HF/HF_merged.bedGraph", sep = "\t")
hf_bed <- hf_bed %>% filter(V1 %in% chromosomename)
hf_bed$V1 <- paste0("chr", hf_bed$V1)
hf_bed$V1[hf_bed$V1 == "chrMT"] <- "chrM"
write_delim(hf_bed, file = "../Rescaled analysis/HF_merged_edited.bedGraph", delim = "\t", col_names = F)
hfk_bed <- read.table("../Rescaled analysis/HFK/HFK_merged.bedGraph", sep = "\t")
hfk_bed <- hfk_bed %>% filter(V1 %in% chromosomename)
hfk_bed$V1 <- paste0("chr", hfk_bed$V1)
hfk_bed$V1[hfk_bed$V1 == "chrMT"] <- "chrM"
write_delim(hfk_bed, file = "../Rescaled analysis/HFK_merged_edited.bedGraph", delim = "\t", col_names = F)# bedGraph to bigWig conversion
$ awk 'NR!=1' HF_merged_edited.bedGraph > input.HF_merged.bedGraph
$ sort -k1,1 -k2,2n input.HF_merged.bedGraph > sorted.input.HF_merged.bedGraph
$ bedGraphToBigWig sorted.input.HF_merged.bedGraph mm10.chrom.sizes HF_merged.bw
$ awk 'NR!=1' HFK_merged_edited.bedGraph > input.HFK_merged.bedGraph
$ sort -k1,1 -k2,2n input.HFK_merged.bedGraph > sorted.input.HFK_merged.bedGraph
$ bedGraphToBigWig sorted.input.HFK_merged.bedGraph mm10.chrom.sizes HFK_merged.bwpeaks <- mirs.peaks
peaks <- lapply(peaks, FUN = function(x) {x[order(x$count, decreasing = T)]})
mybw.dir <- "../Rescaled analysis"
mybw.files <- list.files(mybw.dir, pattern = "bw$", full.names = T)
print(mybw.files)## [1] "../Rescaled analysis/HF_merged.bw" "../Rescaled analysis/HFK_merged.bw"cols <- brewer.pal(name = "Set2", n = 8)
reds <- brewer.pal(name = "Reds", n = 9)
blues <- brewer.pal(name = "Blues", n = 9)
mycolors <-c(blues[6], reds[6]) #HF, HFK
light.colors <- alpha(c(blues[3], reds[2]), 0.4)Histogram
histogram plot function
#plot histograms
peaks_meta <- function(mypeaks = peaks,
miRNA_family = "miR-451a",
dispersion = "se",
dispersion.col = NULL,
coordinates = c(-400, 400),
line.col = mycolors,
winsorize = c(0,99),
title = ""){
suppressWarnings(mypeaks <- lapply(mypeaks, FUN = function(x) {resize(x, width = sum(abs(coordinates)), fix="center")}))
mypeaks <- GRangesList(mypeaks)
mysml <- ScoreMatrixList(targets=mybw.files, window=mypeaks[[miRNA_family]], type = "bigWig", strand.aware = T)
mysampleInfo <- data.frame(basename(mybw.files), c("HF", "HFK"))
names(mysampleInfo) = c("sample", "genotype")
names(mysml) = mysampleInfo$genotype[match(names(mysml), mysampleInfo$sample)]
plotMeta(mysml, profile.names = c("HF", "HFK"), xcoords = coordinates, dispersion = dispersion, main =title, line.col = line.col, winsorize = winsorize, dispersion.col = dispersion.col)
}len <- sapply(mirs.peaks, function(x) length(x))
mirs.peaks <- mirs.peaks[order(-len)]
mirna <- names(mirs.peaks)
for (i in 1:10) {
peaks_meta(mypeaks = peaks, dispersion = NULL, miRNA_family = mirna[i], title = mirna[i])
peaks_meta(mypeaks = peaks, dispersion = "se", miRNA_family = mirna[i], title = mirna[i],
dispersion.col = light.colors)
}
pdf("PDF_figure/Peak_heatmap_merged//Histogram_peak.pdf",
height = 6,
width = 6)
for (i in 1:10) {
peaks_meta(mypeaks = peaks, dispersion = NULL, miRNA_family = mirna[i], title = mirna[i])
peaks_meta(mypeaks = peaks, dispersion = "se", miRNA_family = mirna[i], title = mirna[i],
dispersion.col = light.colors)
}
dev.off()## quartz_off_screen
## 2Heatmap
plot heatmaps
peaks_heat <- function(mypeaks = peaks,
miRNA_family = "miR-451",
col = blues9,
coordinates = c(-400, 400),
order_rows = F,
winsorize_parameters = c(1,98)){
suppressWarnings(mypeaks <- lapply(mypeaks, FUN = function(x) {resize(x, width = sum(abs(coordinates)), fix="center")}))
mypeaks <- GRangesList(mypeaks)
mysml <- ScoreMatrixList(targets=mybw.files[c(1,2)], window=mypeaks[[miRNA_family]], type = "bigWig", strand.aware = T)
mysampleInfo <- data.frame(basename(mybw.files), c("HF", "HFK"))
names(mysampleInfo) = c("sample", "genotype")
names(mysml) = paste(mysampleInfo$genotype[match(names(mysml), mysampleInfo$sample)], miRNA_family , sep = "_")
mysml.scaled = scaleScoreMatrixList(mysml)
#multiHeatMatrix(mysml.scaled, xcoords = coordinates, col = col)
multiHeatMatrix(mysml, common.scale = T, xcoords = coordinates, winsorize = winsorize_parameters, col = col, order = order_rows)
}colfunc <- colorRampPalette(c("white", "blue"))
mycols <- colfunc(128)for (i in 1:10) {
peaks_heat(mypeaks = peaks, miRNA_family = mirna[i], col = blues9, winsorize_parameters = c(0,99))
}
pdf("PDF_figure/Peak_heatmap_merged/Heatmap_peak.pdf",
height = 8,
width = 8)
for (i in 1:10) {
peaks_heat(mypeaks = peaks, miRNA_family = mirna[i], col = blues9, winsorize_parameters = c(0,99))
}
dev.off()## quartz_off_screen
## 2SessionInfo
sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] grid parallel stats4 stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] CLIPanalyze_0.0.10 GenomicAlignments_1.24.0
## [3] Rsamtools_2.4.0 GenomicFeatures_1.40.1
## [5] AnnotationDbi_1.50.3 plyr_1.8.6
## [7] RColorBrewer_1.1-2 genomation_1.20.0
## [9] Biostrings_2.56.0 XVector_0.28.0
## [11] DESeq2_1.28.1 SummarizedExperiment_1.18.2
## [13] DelayedArray_0.14.1 matrixStats_0.59.0
## [15] Biobase_2.48.0 rtracklayer_1.48.0
## [17] GenomicRanges_1.40.0 GenomeInfoDb_1.24.2
## [19] IRanges_2.22.2 S4Vectors_0.26.1
## [21] BiocGenerics_0.34.0 forcats_0.5.1
## [23] stringr_1.4.0 dplyr_1.0.6
## [25] purrr_0.3.4 readr_1.4.0
## [27] tidyr_1.1.3 tibble_3.1.2
## [29] ggplot2_3.3.3 tidyverse_1.3.1
## [31] data.table_1.14.0
##
## loaded via a namespace (and not attached):
## [1] colorspace_2.0-1 ellipsis_0.3.2 fs_1.5.0
## [4] rstudioapi_0.13 farver_2.1.0 bit64_4.0.5
## [7] fansi_0.5.0 lubridate_1.7.10 xml2_1.3.2
## [10] splines_4.0.3 cachem_1.0.5 impute_1.62.0
## [13] geneplotter_1.66.0 knitr_1.33 jsonlite_1.7.2
## [16] seqPattern_1.20.0 broom_0.7.7 gridBase_0.4-7
## [19] annotate_1.66.0 dbplyr_2.1.1 compiler_4.0.3
## [22] httr_1.4.2 biosignals_0.1.0 backports_1.2.1
## [25] assertthat_0.2.1 Matrix_1.3-4 fastmap_1.1.0
## [28] cli_2.5.0 prettyunits_1.1.1 htmltools_0.5.1.1
## [31] tools_4.0.3 gtable_0.3.0 glue_1.4.2
## [34] GenomeInfoDbData_1.2.3 reshape2_1.4.4 rappdirs_0.3.3
## [37] Rcpp_1.0.6 cellranger_1.1.0 jquerylib_0.1.4
## [40] vctrs_0.3.8 xfun_0.23 rvest_1.0.0
## [43] lifecycle_1.0.0 XML_3.99-0.6 zlibbioc_1.34.0
## [46] scales_1.1.1 BSgenome_1.56.0 hms_1.1.0
## [49] curl_4.3.1 yaml_2.2.1 memoise_2.0.0
## [52] sass_0.4.0 biomaRt_2.44.4 stringi_1.6.2
## [55] RSQLite_2.2.7 highr_0.9 genefilter_1.70.0
## [58] plotrix_3.8-1 BiocParallel_1.22.0 rlang_0.4.11
## [61] pkgconfig_2.0.3 bitops_1.0-7 evaluate_0.14
## [64] lattice_0.20-44 bit_4.0.4 tidyselect_1.1.1
## [67] magrittr_2.0.1 R6_2.5.0 generics_0.1.0
## [70] DBI_1.1.1 pillar_1.6.1 haven_2.4.1
## [73] withr_2.4.2 survival_3.2-11 RCurl_1.98-1.3
## [76] modelr_0.1.8 crayon_1.4.1 KernSmooth_2.23-20
## [79] utf8_1.2.1 BiocFileCache_1.12.1 rmarkdown_2.9
## [82] progress_1.2.2 locfit_1.5-9.4 readxl_1.3.1
## [85] blob_1.2.1 reprex_2.0.0 digest_0.6.27
## [88] xtable_1.8-4 openssl_1.4.4 munsell_0.5.0
## [91] bslib_0.2.5.1 askpass_1.1